ROBOTIC PROCESS AUTOMATION (RPA) IN INFRASTRUCTURE MANAGEMENT
Infrastructure Management for Shared Services broadly caters to Monitoring of Events or Alerts, Level 1 and Level 2 support for Apps and Infra, Unified Access Management (UAM) for Infra and Apps servers, DBs and network.
Semi-automated processes are often found in this space via scripting deployed, Ansible applied for Infra automation and other tools like Oracle or Akamai used for UAM.
Challenges of significant impact mostly encountered as listed below:
- Existing tools do some amount of automation however the legacy systems which are still used in Apps space [customer or backend] are not part of the automation wave. They continue to contribute to a great extent of manual intervention. Few such tasks are enlisted below:
- Applications accessed over the network are often not part of the Home Zone. It disables the capacity of automation via the existing tools as there are multiple firewall rules, network zone whitelisting, approvals from outer zone owners to be acquired. This in turn makes it difficult for the deployment of automation if it’s not scalable.
- UAM requests are received in bulk and with a stringent timeline for closure. Collating the requests, creating credentials or access on Active Directory (AD) and other apps or infra server is often repetitive and mundane. However, the error rate is still high with repetitive tickets being raised for closure of requests.
- Network alerts are generated where a spike is observed either due to intrusion or low bandwidth or network loss. This requests if resolved on real time would often improve the uptime of infrastructure. Often the alerts are not looked real time and leads to high wait time to resolve the alerts leading to a business loss.
Few examples of RPA in Infrastructure Management:
- Legacy Systems:
- RPA works on legacy systems, captures data via Image Recognition (IR) or Object cloning if API’s don’t exist. Alerts generated via the systems would be read via RPA and integrates itself with Global Ticketing System to get the tickets logged. RPA can connect with Knowledge Management System’s (KMS) Database (DB), read data from word or excel worksheet or web link to ensure a standard approach is followed to fix the alerts.
- Level 1 monitoring can be completely automated as part of Self-Healing via the BOTs to ensure humans are aligned in reviewing change and problem management instead of mundane activities.
- Network:
- RPA would cater to managing the performance of your network with instant updates and self-healing actions using the platform
- RPA offers a variety of commands like ping, command line to troubleshoot basic performance issues
- RPA can integrate with network configuration management module to manage configuration changes
- Automating configuration backups, adhering to compliance policies and detecting network changes in real-time can be achieved faster with RPA.
- Unified Access Management (UAM):
- RPA would act as an enabler for UAM function where it would alert the end user for incomplete requests filled at the initial stage via email and SMS alerts. This is often possible via integrating UAM modules via API’s or Object cloning practices.
- Logging into server and creating roles often can be performed via BOT via Citrix commands available on RPA platforms
- Testing:
- Virtual workers can be introduced to run same tests over and over and with a scalable BOT environment one can manage tests in the DevOps and UAT cycle
Why is Process Automation the need of the hour?
Automation starts deep within a company’s infrastructure. In fact, the modern CIO’s infrastructure is now largely based on software. Infrastructure-as-code, including containers, has become the new foundation of modern enterprise infrastructures.
Artificial Intelligence (AI) is only one dimension of automation, which also includes many sub-AI-level algorithms and tools. But increasingly, AI suffuses and boosts many different automation technologies – or will do so soon.
While RPA is creating a lot of operational value today, its future lies in cognitive-AI enhancement, a technology convergence that will solve many more business problems and will do so in a more sophisticated manner.
Source: This content is based on a blog by JP Gownder, a principal analyst at Forrester and lead author of “The CIO’s guide to automation, AI and robotics” report.
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